# Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control

## TL;DR

This paper introduces a hybrid decision-making algorithm for automated vehicles at intersections, combining reinforcement learning for high-level decisions with model predictive control for low-level planning, demonstrating improved success rates.

## Contribution

It presents a novel integration of reinforcement learning and model predictive control for intersection navigation in automated vehicles, enhancing decision efficiency and success rate.

## Key findings

- Shorter training episodes achieved
- Higher success rate in intersection negotiation
- Effective handling of diverse driver behaviors

## Abstract

In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on model predictive control. Traffic is simulated with numerous predefined driver behaviors and intentions, and the performance of the proposed decision algorithm was evaluated against another controller. The results show that the proposed decision algorithm yields shorter training episodes and an increased performance in success rate compared to the other controller.